Scalable structural index construction for json analytics

JavaScript Object Notation ( JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices, can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison – a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine

Generalization of tiled models with curved surfaces using typification

Especially for landmark buildings or in the context of cultural heritage documentation, highly detailed digital models are being created in many places. In some of these models, surfaces are represented by tiles which are individually modeled as solid shapes. In many applications, the high complexity of these models has to be reduced for more x efficient visualization and analysis. In our paper, we introduce an approach to derive versions at different scales from such a model through the generalization method of typification that works for curved underlying surfaces. Using the example of tiles placed on a curved roof - which occur, for example, very frequently in ancient Chinese architecture, the original set of tiles is replaced by fewer but bigger tiles while keeping a similar appearance. In the first step, the distribution of the central points of the tiles is approximated by a spline surface. This is necessary because curved roof surfaces cannot be approximated by planes at large scales. After that, the new set of tiles with less rows and/or columns is distributed along a spline surface generated from a morphing of the original surface towards a plane. The degree of morphing is dependent on the desired target scale. If the surface can be represented as a plane at the given resolution, the tiles may be converted to a bump map or a simple texture for visualization. In the final part, a perception-based method using CSF (contrast sensitivity function) is introduced to determine an appropriate LoD (level of detail) version of the model for a given viewing scenario (point of view and camera properties) at runtime.BMBF/GDI-Grid projectNational Basic Reseach Program of China/2010CB731800National High Technology Research and Development Program of China/2008AA12160

LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

Local geometric information, i.e. normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://github.com/tiev-tongji/LOG-LIO.Comment: 8 pages, 4 figure

Scale Estimation with Dual Quadrics for Monocular Object SLAM

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we derive a nonlinear optimization method to make the scaled dimensions of objects conforming to the distribution of their sizes in the physical world, without relying on any prior information of gravity direction. We adopt the dual quadric to represent objects for its ability to fit objects compactly and accurately. In the proposed monocular object-level SLAM system, dual quadrics are fastly initialized based on constraints of 2-D detections and fitted oriented bounding box and are further optimized to provide reliable dimensions for scale estimation.Comment: 8 pages, 6 figures, accepted by IROS202

Learning Sequence Descriptor based on Spatiotemporal Attention for Visual Place Recognition

Sequence-based visual place recognition (sVPR) aims to match frame sequences with frames stored in a reference map for localization. Existing methods include sequence matching and sequence descriptor-based retrieval. The former is based on the assumption of constant velocity, which is difficult to hold in real scenarios and does not get rid of the intrinsic single frame descriptor mismatch. The latter solves this problem by extracting a descriptor for the whole sequence, but current sequence descriptors are only constructed by feature aggregation of multi-frames, with no temporal information interaction. In this paper, we propose a sequential descriptor extraction method to fuse spatiotemporal information effectively and generate discriminative descriptors. Specifically, similar features on the same frame focu on each other and learn space structure, and the same local regions of different frames learn local feature changes over time. And we use sliding windows to control the temporal self-attention range and adpot relative position encoding to construct the positional relationships between different features, which allows our descriptor to capture the inherent dynamics in the frame sequence and local feature motion